Targeted Undersmoothing: Sensitivity Analysis for Sparse Estimators

Review of Economics and Statistics ◽

10.1162/rest_a_01017 ◽

2021 ◽

pp. 1-43

Author(s):

Christian Hansen ◽

Damian Kozbur ◽

Sanjog Misra

Keyword(s):

Sensitivity Analysis ◽

Model Selection ◽

High Dimensional ◽

Parameter Vector ◽

Simulation Experiments ◽

Conduct Sensitivity Analysis ◽

Dimensional Models ◽

Assessing Sensitivity

This paper proposes a procedure for assessing sensitivity of inferential conclusions for functionals of sparse high-dimensional models following model selection. The proposed procedure is called targeted undersmoothing. Functionals considered include dense functionals that may depend on many or all elements of the highdimensional parameter vector. The sensitivity analysis is based on systematic enlargements of an initially selected model. By varying the enlargements, one can conduct sensitivity analysis about the strength of empirical conclusions to model selection mistakes. We illustrate the procedure's performance through simulation experiments and two empirical examples.

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Model Selection for High-Dimensional Models

Handbook of Survival Analysis ◽

10.1201/b16248-24 ◽

2016 ◽

pp. 309-330

Keyword(s):

Model Selection ◽

High Dimensional ◽

Selection For ◽

Dimensional Models

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A Model Selection Approach to the Two-Phase Regression Estimation and The Human Sensitivity Analysis in Urban Ecosystem

Behaviormetrika ◽

10.2333/bhmk.10.13_1 ◽

1983 ◽

Vol 10 (13) ◽

pp. 1-18

Author(s):

Masashi Itoh ◽

Kazuo Noda ◽

Yutaka Shinada ◽

Naomi Tachibana

Keyword(s):

Sensitivity Analysis ◽

Model Selection ◽

Urban Ecosystem ◽

Regression Estimation ◽

Two Phase ◽

Model Selection Approach ◽

Selection Approach

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US Stock return predictability with high dimensional models

Finance Research Letters ◽

10.1016/j.frl.2021.102194 ◽

2021 ◽

pp. 102194

Author(s):

Afees A. Salisu ◽

Jean Paul Tchankam

Keyword(s):

Stock Return ◽

Return Predictability ◽

High Dimensional ◽

Stock Return Predictability ◽

Dimensional Models

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Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Energies ◽

10.3390/en13174290 ◽

2020 ◽

Vol 13 (17) ◽

pp. 4290

Author(s):

Dongmei Zhang ◽

Yuyang Zhang ◽

Bohou Jiang ◽

Xinwei Jiang ◽

Zhijiang Kang

Keyword(s):

Sensitivity Analysis ◽

Gaussian Processes ◽

Latent Variable ◽

History Matching ◽

Latent Variable Models ◽

High Dimensional ◽

Model Parameters ◽

Variable Model ◽

Assisted History Matching ◽

Proxy Models

Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

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Inference for treatment effect parameters in potentially misspecified high-dimensional models

Biometrika ◽

10.1093/biomet/asaa071 ◽

2020 ◽

Author(s):

Oliver Dukes ◽

Stijn Vansteelandt

Keyword(s):

Sample Size ◽

Confidence Intervals ◽

Treatment Effect ◽

Observational Studies ◽

Treatment Effects ◽

Treatment Selection ◽

High Dimensional ◽

Selection Mechanism ◽

Dimensional Models ◽

Effect Parameter

Summary Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naïve use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates grows with the sample size, correctly specifying a model for the outcome is nontrivial. In this article we deal with both of these concerns simultaneously, obtaining confidence intervals for conditional treatment effects that are uniformly valid, regardless of whether the outcome model is correct. This is done by incorporating an additional model for the treatment selection mechanism. When both models are correctly specified, we can weaken the standard conditions on model sparsity. Our procedure extends to multivariate treatment effect parameters and complex longitudinal settings.

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